/*
* NeuQuant Neural-Net Quantization Algorithm
* ------------------------------------------
*
* Copyright (c) 1994 Anthony Dekker
*
* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
* "Kohonen neural networks for optimal colour quantization" in "Network:
* Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
* the algorithm.
*
* Any party obtaining a copy of these files from the author, directly or
* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
* world-wide, paid up, royalty-free, nonexclusive right and license to deal in
* this software and documentation files (the "Software"), including without
* limitation the rights to use, copy, modify, merge, publish, distribute,
* sublicense, and/or sell copies of the Software, and to permit persons who
* receive copies from any such party to do so, with the only requirement being
* that this copyright notice remain intact.
*/

/*
* This class handles Neural-Net quantization algorithm
* @author Kevin Weiner (original Java version - kweiner@fmsware.com)
* @author Thibault Imbert (AS3 version - bytearray.org)
* @version 0.1 AS3 implementation
*/

  //import flash.utils.ByteArray;

  NeuQuant = function()
  {
      var exports = {};
    /*private_static*/ var netsize/*int*/ = 256; /* number of colours used */

    /* four primes near 500 - assume no image has a length so large */
    /* that it is divisible by all four primes */

    /*private_static*/ var prime1/*int*/ = 499;
    /*private_static*/ var prime2/*int*/ = 491;
    /*private_static*/ var prime3/*int*/ = 487;
    /*private_static*/ var prime4/*int*/ = 503;
    /*private_static*/ var minpicturebytes/*int*/ = (3 * prime4);

    /* minimum size for input image */
    /*
    * Program Skeleton ---------------- [select samplefac in range 1..30] [read
    * image from input file] pic = (unsigned char*) malloc(3*width*height);
    * initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output
    * image header, using writecolourmap(f)] inxbuild(); write output image using
    * inxsearch(b,g,r)
    */

    /*
    * Network Definitions -------------------
    */

    /*private_static*/ var maxnetpos/*int*/ = (netsize - 1);
    /*private_static*/ var netbiasshift/*int*/ = 4; /* bias for colour values */
    /*private_static*/ var ncycles/*int*/ = 100; /* no. of learning cycles */

    /* defs for freq and bias */
    /*private_static*/ var intbiasshift/*int*/ = 16; /* bias for fractions */
    /*private_static*/ var intbias/*int*/ = (1 << intbiasshift);
    /*private_static*/ var gammashift/*int*/ = 10; /* gamma = 1024 */
    /*private_static*/ var gamma/*int*/ = (1 << gammashift);
    /*private_static*/ var betashift/*int*/ = 10;
    /*private_static*/ var beta/*int*/ = (intbias >> betashift); /* beta = 1/1024 */
    /*private_static*/ var betagamma/*int*/ = (intbias << (gammashift - betashift));

    /* defs for decreasing radius factor */
    /*private_static*/ var initrad/*int*/ = (netsize >> 3); /*
                                                           * for 256 cols, radius
                                                           * starts
                                                           */

    /*private_static*/ var radiusbiasshift/*int*/ = 6; /* at 32.0 biased by 6 bits */
    /*private_static*/ var radiusbias/*int*/ = (1 << radiusbiasshift);
    /*private_static*/ var initradius/*int*/ = (initrad * radiusbias); /*
                                                                     * and
                                                                     * decreases
                                                                     * by a
                                                                     */

    /*private_static*/ var radiusdec/*int*/ = 30; /* factor of 1/30 each cycle */

    /* defs for decreasing alpha factor */
    /*private_static*/ var alphabiasshift/*int*/ = 10; /* alpha starts at 1.0 */
    /*private_static*/ var initalpha/*int*/ = (1 << alphabiasshift);
    /*private*/ var alphadec/*int*/ /* biased by 10 bits */

    /* radbias and alpharadbias used for radpower calculation */
    /*private_static*/ var radbiasshift/*int*/ = 8;
    /*private_static*/ var radbias/*int*/ = (1 << radbiasshift);
    /*private_static*/ var alpharadbshift/*int*/ = (alphabiasshift + radbiasshift);

    /*private_static*/ var alpharadbias/*int*/ = (1 << alpharadbshift);

    /*
    * Types and Global Variables --------------------------
    */

    /*private*/ var thepicture/*ByteArray*//* the input image itself */
    /*private*/ var lengthcount/*int*/; /* lengthcount = H*W*3 */
    /*private*/ var samplefac/*int*/; /* sampling factor 1..30 */

    // typedef int pixel[4]; /* BGRc */
    /*private*/ var network/*Array*/; /* the network itself - [netsize][4] */
    /*protected*/ var netindex/*Array*/ = new Array();

    /* for network lookup - really 256 */
    /*private*/ var bias/*Array*/ = new Array();

    /* bias and freq arrays for learning */
    /*private*/ var freq/*Array*/ = new Array();
    /*private*/ var radpower/*Array*/ = new Array();

    var NeuQuant = exports.NeuQuant = function NeuQuant(thepic/*ByteArray*/, len/*int*/, sample/*int*/)
    {

      var i/*int*/;
      var p/*Array*/;

      thepicture = thepic;
      lengthcount = len;
      samplefac = sample;

      network = new Array(netsize);

      for (i = 0; i < netsize; i++)
      {

        network[i] = new Array(4);
        p = network[i];
        p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
        freq[i] = intbias / netsize; /* 1/netsize */
        bias[i] = 0;
      }

    }

    var colorMap = function colorMap()/*ByteArray*/
    {

      var map/*ByteArray*/ = [];
        var index/*Array*/ = new Array(netsize);
        for (var i/*int*/ = 0; i < netsize; i++)
          index[network[i][3]] = i;
        var k/*int*/ = 0;
        for (var l/*int*/ = 0; l < netsize; l++) {
          var j/*int*/ = index[l];
          map[k++] = (network[j][0]);
          map[k++] = (network[j][1]);
          map[k++] = (network[j][2]);
        }
        return map;

    }

    /*
     * Insertion sort of network and building of netindex[0..255] (to do after
     * unbias)
     * -------------------------------------------------------------------------------
     */

     var inxbuild = function inxbuild()/*void*/
     {

      var i/*int*/;
      var j/*int*/;
      var smallpos/*int*/;
      var smallval/*int*/;
      var p/*Array*/;
      var q/*Array*/;
      var previouscol/*int*/
      var startpos/*int*/

      previouscol = 0;
      startpos = 0;
      for (i = 0; i < netsize; i++)
      {

        p = network[i];
        smallpos = i;
        smallval = p[1]; /* index on g */
        /* find smallest in i..netsize-1 */
        for (j = i + 1; j < netsize; j++)
        {
          q = network[j];
          if (q[1] < smallval)
          { /* index on g */

          smallpos = j;
          smallval = q[1]; /* index on g */
        }
        }

        q = network[smallpos];
        /* swap p (i) and q (smallpos) entries */

        if (i != smallpos)
        {

          j = q[0];
          q[0] = p[0];
          p[0] = j;
          j = q[1];
          q[1] = p[1];
          p[1] = j;
          j = q[2];
          q[2] = p[2];
          p[2] = j;
          j = q[3];
          q[3] = p[3];
          p[3] = j;

        }

        /* smallval entry is now in position i */

        if (smallval != previouscol)

        {

        netindex[previouscol] = (startpos + i) >> 1;

        for (j = previouscol + 1; j < smallval; j++) netindex[j] = i;

        previouscol = smallval;
        startpos = i;

        }

      }

      netindex[previouscol] = (startpos + maxnetpos) >> 1;
      for (j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; /* really 256 */

     }

     /*
     * Main Learning Loop ------------------
     */

     var learn = function learn()/*void*/

     {

       var i/*int*/;
       var j/*int*/;
       var b/*int*/;
       var g/*int*/
       var r/*int*/;
       var radius/*int*/;
       var rad/*int*/;
       var alpha/*int*/;
       var step/*int*/;
       var delta/*int*/;
       var samplepixels/*int*/;
       var p/*ByteArray*/;
       var pix/*int*/;
       var lim/*int*/;

       if (lengthcount < minpicturebytes) samplefac = 1;

       alphadec = 30 + ((samplefac - 1) / 3);
       p = thepicture;
       pix = 0;
       lim = lengthcount;
       samplepixels = lengthcount / (3 * samplefac);
       delta = (samplepixels / ncycles) | 0;
       alpha = initalpha;
       radius = initradius;

       rad = radius >> radiusbiasshift;
       if (rad <= 1) rad = 0;

       for (i = 0; i < rad; i++) radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));


       if (lengthcount < minpicturebytes) step = 3;

       else if ((lengthcount % prime1) != 0) step = 3 * prime1;

       else

       {

         if ((lengthcount % prime2) != 0) step = 3 * prime2;

         else

         {

           if ((lengthcount % prime3) != 0) step = 3 * prime3;

           else step = 3 * prime4;

         }

       }

       i = 0;

       while (i < samplepixels)

       {

         b = (p[pix + 0] & 0xff) << netbiasshift;
         g = (p[pix + 1] & 0xff) << netbiasshift;
         r = (p[pix + 2] & 0xff) << netbiasshift;
         j = contest(b, g, r);

         altersingle(alpha, j, b, g, r);

         if (rad != 0) alterneigh(rad, j, b, g, r); /* alter neighbours */

         pix += step;

         if (pix >= lim) pix -= lengthcount;

         i++;

         if (delta == 0) delta = 1;

         if (i % delta == 0)

         {

           alpha -= alpha / alphadec;
           radius -= radius / radiusdec;
           rad = radius >> radiusbiasshift;

           if (rad <= 1) rad = 0;

           for (j = 0; j < rad; j++) radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));

         }

       }

     }

     /*
     ** Search for BGR values 0..255 (after net is unbiased) and return colour
     * index
     * ----------------------------------------------------------------------------
     */

     var map = exports.map = function map(b/*int*/, g/*int*/, r/*int*/)/*int*/

     {

       var i/*int*/;
       var j/*int*/;
       var dist/*int*/
       var a/*int*/;
       var bestd/*int*/;
       var p/*Array*/;
       var best/*int*/;

       bestd = 1000; /* biggest possible dist is 256*3 */
       best = -1;
       i = netindex[g]; /* index on g */
       j = i - 1; /* start at netindex[g] and work outwards */

      while ((i < netsize) || (j >= 0))

    {

      if (i < netsize)

      {

        p = network[i];

        dist = p[1] - g; /* inx key */

        if (dist >= bestd) i = netsize; /* stop iter */

        else

        {

          i++;

          if (dist < 0) dist = -dist;

          a = p[0] - b;

          if (a < 0) a = -a;

          dist += a;

          if (dist < bestd)

          {

            a = p[2] - r;

            if (a < 0) a = -a;

            dist += a;

            if (dist < bestd)

            {

              bestd = dist;
              best = p[3];

            }

          }

        }

      }

        if (j >= 0)
      {

        p = network[j];

        dist = g - p[1]; /* inx key - reverse dif */

        if (dist >= bestd) j = -1; /* stop iter */

        else
        {

          j--;
          if (dist < 0) dist = -dist;
          a = p[0] - b;
          if (a < 0) a = -a;
          dist += a;

          if (dist < bestd)

          {

            a = p[2] - r;
            if (a < 0)a = -a;
            dist += a;
            if (dist < bestd)
            {
              bestd = dist;
              best = p[3];
            }

          }

        }

      }

    }

      return (best);

    }

    var process = exports.process = function process()/*ByteArray*/
    {

      learn();
      unbiasnet();
      inxbuild();
      return colorMap();

    }

    /*
    * Unbias network to give byte values 0..255 and record position i to prepare
    * for sort
    * -----------------------------------------------------------------------------------
    */

    var unbiasnet = function unbiasnet()/*void*/

    {

      var i/*int*/;
      var j/*int*/;

      for (i = 0; i < netsize; i++)
    {
        network[i][0] >>= netbiasshift;
        network[i][1] >>= netbiasshift;
        network[i][2] >>= netbiasshift;
        network[i][3] = i; /* record colour no */
      }

    }

    /*
    * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in
    * radpower[|i-j|]
    * ---------------------------------------------------------------------------------
    */

    var alterneigh = function alterneigh(rad/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/

    {

      var j/*int*/;
      var k/*int*/;
      var lo/*int*/;
      var hi/*int*/;
      var a/*int*/;
      var m/*int*/;

      var p/*Array*/;

      lo = i - rad;
      if (lo < -1) lo = -1;

      hi = i + rad;

      if (hi > netsize) hi = netsize;

      j = i + 1;
      k = i - 1;
      m = 1;

      while ((j < hi) || (k > lo))

      {

        a = radpower[m++];

        if (j < hi)

        {

          p = network[j++];

          try {

            p[0] -= (a * (p[0] - b)) / alpharadbias;
            p[1] -= (a * (p[1] - g)) / alpharadbias;
            p[2] -= (a * (p[2] - r)) / alpharadbias;

            } catch (e/*Error*/) {} // prevents 1.3 miscompilation

        }

        if (k > lo)

        {

          p = network[k--];

          try
          {

            p[0] -= (a * (p[0] - b)) / alpharadbias;
            p[1] -= (a * (p[1] - g)) / alpharadbias;
            p[2] -= (a * (p[2] - r)) / alpharadbias;

          } catch (e/*Error*/) {}

        }

      }

    }

    /*
    * Move neuron i towards biased (b,g,r) by factor alpha
    * ----------------------------------------------------
    */

    var altersingle = function altersingle(alpha/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/
    {

      /* alter hit neuron */
      var n/*Array*/ = network[i];
      n[0] -= (alpha * (n[0] - b)) / initalpha;
      n[1] -= (alpha * (n[1] - g)) / initalpha;
      n[2] -= (alpha * (n[2] - r)) / initalpha;

    }

    /*
    * Search for biased BGR values ----------------------------
    */

    var contest = function contest(b/*int*/, g/*int*/, r/*int*/)/*int*/
    {

      /* finds closest neuron (min dist) and updates freq */
      /* finds best neuron (min dist-bias) and returns position */
      /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
      /* bias[i] = gamma*((1/netsize)-freq[i]) */

      var i/*int*/;
      var dist/*int*/;
      var a/*int*/;
      var biasdist/*int*/;
      var betafreq/*int*/;
      var bestpos/*int*/;
      var bestbiaspos/*int*/;
      var bestd/*int*/;
      var bestbiasd/*int*/;
      var n/*Array*/;

      bestd = ~(1 << 31);
      bestbiasd = bestd;
      bestpos = -1;
      bestbiaspos = bestpos;

      for (i = 0; i < netsize; i++)

      {

        n = network[i];
        dist = n[0] - b;

        if (dist < 0) dist = -dist;

        a = n[1] - g;

        if (a < 0) a = -a;

        dist += a;

        a = n[2] - r;

        if (a < 0) a = -a;

        dist += a;

        if (dist < bestd)

        {

          bestd = dist;
          bestpos = i;

        }

        biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));

        if (biasdist < bestbiasd)

        {

          bestbiasd = biasdist;
          bestbiaspos = i;

        }

        betafreq = (freq[i] >> betashift);
        freq[i] -= betafreq;
        bias[i] += (betafreq << gammashift);

      }

      freq[bestpos] += beta;
      bias[bestpos] -= betagamma;
      return (bestbiaspos);

    }

    NeuQuant.apply(this, arguments);
    return exports;
  }